Report Sleep Deprivation

Description

This is the report of the analysis made for the paper (TITLE) AND AUTHORS. INSERT ABSTRACT

Importing data

Importing data and filtering out those genes with cpm lesser than 1. We use the filtered.data method in NOISeq package.

countMatrix <- ReadDataFrameFromTsv(file.name.path="../../data/refSEQ_countMatrix.txt")

## ../../data/refSEQ_countMatrix.txt read from disk!

# head(countMatrix)

designMatrix <- ReadDataFrameFromTsv(file.name.path="../../design/all_samples_short_names_noRS2HC7.tsv")

## ../../design/all_samples_short_names_noRS2HC7.tsv read from disk!

# head(designMatrix)

filteredCountsProp <- filterLowCounts(counts.dataframe=countMatrix, 
                                    is.normalized=FALSE,
                                    design.dataframe=designMatrix,
                                    cond.col.name="gcondition",
                                    method.type="Proportion")

## features dimensions before normalization: 27179

## Filtering out low count features...
## 14454 features are to be kept for differential expression analysis with filtering method 3

Plot PCA of log unnormalized data

PCA Plot of filtered not-normalized data.

PlotPCAPlotlyFunction(counts.data.frame=log1p(filteredCountsProp), 
    design.matrix=designMatrix, 
    shapeColname="condition", colorColname="genotype", xPCA="PC1", yPCA="PC2", 
    plotly.flag=TRUE, show.plot.flag=TRUE, prefix.plot="Prop-Un-Norm")

## [1] TRUE

Control Genes

Negative control genes

Loading Negative Control Genes to normalize data

library(readxl)

sd.ctrls <- read_excel(path="../../data/controls/Additional File 4 full list of BMC genomics SD&RS2.xlsx", sheet=1)
sd.ctrls <- sd.ctrls[order(sd.ctrls$adj.P.Val),]

sd.neg.ctrls <- sd.ctrls[sd.ctrls$adj.P.Val > 0.9, ]

sd.neg.ctrls <- sd.neg.ctrls$`MGI Symbol`
sd.neg.ctrls <- sd.neg.ctrls[-which(is.na(sd.neg.ctrls))]

int.neg.ctrls <- sd.neg.ctrls
int.neg.ctrls <- unique(int.neg.ctrls)
neg.map <- convertGenesViaMouseDb(gene.list=int.neg.ctrls, fromType="SYMBOL",
                                "ENTREZID")
# sum(is.na(neg.map$ENTREZID))
neg.ctrls.entrez <- as.character(neg.map$ENTREZID)


ind.ctrls <- which(rownames(filteredCountsProp) %in% neg.ctrls.entrez)
counts.neg.ctrls <- filteredCountsProp[ind.ctrls,]

positive control genes

Loading Positive Control Genes to detect them during the differential expression step.

## sleep deprivation
sd.lit.pos.ctrls <- read_excel("../../data/controls/SD_RS_PosControls_final.xlsx", 
                        sheet=1)
colnames(sd.lit.pos.ctrls) <- sd.lit.pos.ctrls[1,]
sd.lit.pos.ctrls <- sd.lit.pos.ctrls[-1,]


sd.est.pos.ctrls <- read_excel("../../data/controls/SD_RS_PosControls_final.xlsx", 
                        sheet=3)

sd.pos.ctrls <- cbind(sd.est.pos.ctrls$`MGI Symbol`, "est")
sd.pos.ctrls <- rbind(sd.pos.ctrls, cbind(sd.lit.pos.ctrls$Gene, "lit"))

sd.pos.ctrls <- sd.pos.ctrls[-which(duplicated(sd.pos.ctrls[,1])),]
sd.pos.ctrls <- sd.pos.ctrls[-which(is.na(sd.pos.ctrls[,1])),]

Normalizations

TMM Normalization

Normalizing data with TMM, as implemented in edgeR package, and plotting a PCA and an RLE plot of them.

normPropCountsUqua <- NormalizeData(data.to.normalize=filteredCountsProp, 
                                    norm.type="tmm", 
                                    design.matrix=designMatrix)

PlotPCAPlotlyFunction(counts.data.frame=log1p(normPropCountsUqua), 
                    design.matrix=designMatrix, shapeColname="condition", 
                    colorColname="genotype", xPCA="PC1", yPCA="PC2", 
                    plotly.flag=TRUE, show.plot.flag=TRUE, 
                    prefix.plot="TMM-Norm")

## [1] TRUE

pal <- RColorBrewer::brewer.pal(9, "Set1")
plotRLE(as.matrix(normPropCountsUqua), outline=FALSE, col=pal[designMatrix$gcondition])

TMM + RUVs Normalization

Applying a RUVs method of RUVSeq package on normalized data, in order to adjust the counts for the unwanted variation. And of corse we plot a PCA and an RLE plot on these data.

library(RUVSeq)
neg.ctrl.list <- rownames(counts.neg.ctrls)
groups <- makeGroups(designMatrix$gcondition)
ruvedSExprData <- RUVs(as.matrix(round(normPropCountsUqua)), cIdx=neg.ctrl.list,
                       scIdx=groups, k=5)

normExprData <- ruvedSExprData$normalizedCounts

ggp <- PlotPCAPlotlyFunction(counts.data.frame=log1p(normExprData), 
                    design.matrix=designMatrix, shapeColname="condition", 
                    colorColname="genotype", xPCA="PC1", yPCA="PC2", 
                    plotly.flag=FALSE, show.plot.flag=FALSE, save.plot=FALSE,
                    prefix.plot=NULL)

## [1] FALSE

ggplotly(ggp)

dir.create("plots")
save_plot(filename="plots/PCA.pdf", plot=ggp)

pal <- RColorBrewer::brewer.pal(9, "Set1")
plotRLE(normExprData, outline=FALSE, col=pal[designMatrix$gcondition])

edgeR Differential Expression Analysis

Making differential expression analysis with edgeR package on four different contrasts.

Here is a brief legend:

• WTHC5: Wild Type Home Cage Control 5 days
• WTSD5: Wild Type Sleep Deprivation 5 days.
• KOHC5: Knock Out Home Cage Control 5 days.
• KOSD5: Knock Out Sleep Deprivation 5 days.

padj.thr <- 0.05
venn.padgj.thr <- 0.1
desMat <- cbind(designMatrix, ruvedSExprData$W)
colnames(desMat) <- c(colnames(designMatrix), colnames(ruvedSExprData$W))

cc <- c("S3HC5 - WTHC5", "S3SD5 - WTSD5")

rescList1 <- applyEdgeR(counts=filteredCountsProp, design.matrix=desMat,
                        factors.column="gcondition", 
                        weight.columns=c("W_1", "W_2", "W_3", "W_4", "W_5"),
                        contrasts=cc, useIntercept=FALSE, p.threshold=1,
                        is.normalized=FALSE, verbose=TRUE)
names <- names(rescList1)
rescList1 <- lapply(seq_along(rescList1), function(i) 
{
    attachMeans(normalized.counts=normExprData, design.matrix=desMat, 
                factor.column="gcondition", contrast.name=names(rescList1)[i],
                de.results=rescList1[[i]])
})
names(rescList1) <- names

Shank3 Home Cage control VS Wild Type Home Cage Controls

volcano plot

A volcano plot of differential expressed genes.

res.o.map1 <- convertGenesViaMouseDb(gene.list=rownames(rescList1[[1]]), 
                            fromType="ENTREZID")

res.o <- attachGeneColumnToDf(mainDf=rescList1[[1]],
                                genesMap=res.o.map1,
                                rowNamesIdentifier="ENTREZID",
                                mapFromIdentifier="ENTREZID",
                                mapToIdentifier="SYMBOL")
WriteDataFrameAsTsv(data.frame.to.save=res.o, 
                    file.name.path=paste0(names(rescList1)[1], "_edgeR"))

vp <- luciaVolcanoPlot(res.o, prefix=names(rescList1)[1], 
                        positive.controls.df=NULL,
                        threshold=padj.thr)
ggplotly(vp)

de <- sum(res.o$FDR < padj.thr)
nde <- sum(res.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[1]
ddetable <- detable

tot.ctrls <- dim(sd.pos.ctrls)[1]
idx.pc <- which(tolower(res.o$gene) %in% tolower(sd.pos.ctrls[,1]))
tot.pc.de <- sum(res.o$FDR[idx.pc] < padj.thr)
tot.pc.nde <- length(idx.pc) - tot.pc.de

wt <- res.o[which(res.o$FDR < padj.thr),]
wt.sign.genes.entrez <- rownames(res.o)[which(res.o$FDR < venn.padgj.thr)]

kowthc5 <- res.o[which(res.o$FDR < padj.thr),]
kowthc5.sign.genes.entrez <- rownames(res.o)[which(res.o$FDR < venn.padgj.thr)]

Shank3 Sleed Deprivation VS Wild Type Sleep Deprivation

volcano plot

A volcano plot of differential expressed genes.

rs2.o.map <- convertGenesViaMouseDb(gene.list=rownames(rescList1[[2]]), 
                            fromType="ENTREZID")

res.rs2.o <- attachGeneColumnToDf(mainDf=rescList1[[2]],
                                genesMap=rs2.o.map,
                                rowNamesIdentifier="ENTREZID",
                                mapFromIdentifier="ENTREZID",
                                mapToIdentifier="SYMBOL")
WriteDataFrameAsTsv(data.frame.to.save=res.rs2.o, 
                    file.name.path=paste0(names(rescList1)[2], "_edgeR"))

vp <- luciaVolcanoPlot(res.rs2.o, positive.controls.df=sd.pos.ctrls,
                       prefix=names(rescList1)[2], 
                       threshold=padj.thr)
ggplotly(vp)

de <- sum(res.rs2.o$FDR < padj.thr)
nde <- sum(res.rs2.o$FDR >= padj.thr)
detable <- cbind(de,nde)
rownames(detable) <- names(rescList1)[2]
ddetable <- rbind(ddetable, detable)
pos.df <- cbind(tot.ctrls, tot.pc.de, tot.pc.nde)
colnames(pos.df) <- c("total_p.ctrl", "p.ctrl_de_mapped", 
                    "p.ctrl_notde_mapped")
rownames(pos.df) <- names(rescList1)[2]

kowtsd5 <- res.rs2.o[which(res.rs2.o$FDR < padj.thr),]
kowtsd5.sign.genes.entrez <- rownames(res.rs2.o)[which(res.rs2.o$FDR < venn.padgj.thr)]

DE TABLE + Positive Controls table

We present a summarization of the results. The first table is a summarization on how many genes are Differentially Expressed. The second table explains on the first column how many positive controls we have, on the second column how many positive controls have been identified over the differentially expressed genes, and, finally, on the third column how many positive controls have beed identified on the NOT differentially expressed genes.

ddetable

##               de   nde
## S3HC5 - WTHC5 39 14415
## S3SD5 - WTSD5 82 14372

pos.df

##               total_p.ctrl p.ctrl_de_mapped p.ctrl_notde_mapped
## S3SD5 - WTSD5          579                3                 553

Venn Diagram

KOHC5-WTHC5 vs KOSD5-WTSD5

We take the results of the two contrasts. Knock Out Sleed Deprivation VS Wild Type Sleep Deprivation and Knock Out Home Cage control VS Wild Type Home Cage Controls . And plot the results in a Venn Diagram

source("../../R/venn2.R")

gene.map <- convertGenesViaMouseDb(gene.list=rownames(normExprData),
                                   fromType="ENTREZID", toType="SYMBOL")
venn <- Venn2de(x=kowthc5.sign.genes.entrez, y=kowtsd5.sign.genes.entrez, 
        label1="S3HC5_WTHC5", label2="S3SD5_WTSD5",
        title="S3HC5_WTHC5 venn S3SD5_WTSD5", plot.dir="./",
        conversion.map=gene.map)

Heatmaps

Setting up the data structures for the heatmps.

source("../../R/heatmapFunctions.R")
de.genes.entr <- union(rownames(venn$int), rownames(venn$XnoY))
de.genes.entr <- union(de.genes.entr, rownames(venn$YnoX))

gene.map <- convertGenesViaMouseDb(gene.list=de.genes.entr, 
                            fromType="ENTREZID")
de.genes.symb <- attachGeneColumnToDf(as.data.frame(de.genes.entr, 
                                                    row.names=de.genes.entr), 
                                    genesMap=gene.map, 
                                    rowNamesIdentifier="ENTREZID", 
                                    mapFromIdentifier="ENTREZID",
                                    mapToIdentifier="SYMBOL")

# de.genes.symb[which(is.na(de.genes.symb$gene)),]
de.genes.symb$gene[which(de.genes.symb$de.genes.entr=="100039826")]  <- "Gm2444" ## not annotated in ncbi
de.genes.symb$gene[which(de.genes.symb$de.genes.entr=="210541")]  <- "Entrez:210541" ## not annotated in ncbi


de.genes.counts <- normExprData[match(de.genes.symb$de.genes.entr, rownames(normExprData)),]
rownames(de.genes.counts) <- de.genes.symb$gene

de.gene.means <- computeGeneMeansOverGroups(counts=de.genes.counts, 
                            design=designMatrix, groupColumn="gcondition")

library(gplots)
library(clusterExperiment)
color.palette = clusterExperiment::seqPal3#c("black", "yellow")
pal <- colorRampPalette(color.palette)(n = 1000)


library(pheatmap)
filter2 <- rowMeans(de.gene.means)>0
filter <- apply(de.gene.means, 1, function(x) log(x[4]/x[3]) * log(x[2]/x[1]) < 0)
filter[is.na(filter)] <- FALSE

Kmeans k=4

With k=4 the clusters seem to be balanced and the samples are very well clustered for each combination of genotype and condition.

ph1 <- pheatmap(log(de.genes.counts[filter2,]+1), cluster_cols=TRUE, scale="row", 
            color=pal, border_color=NA, fontsize_row=10, kmeans_k=4)

save_pheatmap_pdf(filename="plots/heatmap_kmeans_k4.pdf", plot=ph1)

## png 
##   2

clusterized.genes <- as.data.frame(ph1$kmeans$cluster)

gene.map <- convertGenesViaMouseDb(gene.list=rownames(clusterized.genes), fromType="SYMBOL")
converted.clusterized.gens <- attachGeneColumnToDf(mainDf=clusterized.genes, genesMap=gene.map, 
                    rowNamesIdentifier="SYMBOL", mapFromIdentifier="SYMBOL", mapToIdentifier="ENTREZID")

converted.clusterized.gens$gene[which(rownames(converted.clusterized.gens)=="Gm2444")]  <- "100039826" ## not annotated in ncbi
converted.clusterized.gens$gene[which(rownames(converted.clusterized.gens)=="Entrez:210541")]  <- "210541" ## not annotated in ncbi
converted.clusterized.gens <- converted.clusterized.gens[order(converted.clusterized.gens$`ph1$kmeans$cluster`),]
WriteDataFrameAsTsv(data.frame.to.save=converted.clusterized.gens, file.name.path="plots/clustered_genes_by_kmeans4")

Kmeans k=5

with k=5 the clusters seem to be a bit unbalanced and the samples seem not so well clustered.

ph1 <- pheatmap(log(de.genes.counts[filter2,]+1), cluster_cols=TRUE, scale="row", 
            color=pal, border_color=NA, fontsize_row=10, kmeans_k=5)

save_pheatmap_pdf(filename="plots/heatmap_kmeans_k5.pdf", plot=ph1)

## png 
##   2

clusterized.genes <- as.data.frame(ph1$kmeans$cluster)

gene.map <- convertGenesViaMouseDb(gene.list=rownames(clusterized.genes), fromType="SYMBOL")
converted.clusterized.gens <- attachGeneColumnToDf(mainDf=clusterized.genes, genesMap=gene.map, 
                    rowNamesIdentifier="SYMBOL", mapFromIdentifier="SYMBOL", mapToIdentifier="ENTREZID")

converted.clusterized.gens$gene[which(rownames(converted.clusterized.gens)=="Gm2444")]  <- "100039826" ## not annotated in ncbi
converted.clusterized.gens$gene[which(rownames(converted.clusterized.gens)=="Entrez:210541")]  <- "210541" ## not annotated in ncbi
converted.clusterized.gens <- converted.clusterized.gens[order(converted.clusterized.gens$`ph1$kmeans$cluster`),]
WriteDataFrameAsTsv(data.frame.to.save=converted.clusterized.gens, file.name.path="plots/clustered_genes_by_kmeans5")

Group gene profiles

Group gene profiles by genotype

g <- geneGroupProfileRows(normalized.counts=normExprData, design.matrix=designMatrix,
            gene.names=c("Nr1d1", "Fabp7", "Per3"),
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE, log.flag=FALSE)
ggplotly(g)

save_plot(filename=paste0("plots/", "Nr1d1_Fabp7_Per3", "_log_gene_profile_genotype.pdf"), plot=g)

g <- geneGroupProfileRows(normalized.counts=normExprData, design.matrix=designMatrix,
            gene.names=c("Jun", "Elk1", "Fosl2", "Mapk1", "Mapk3", "Mapk11"),
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE, log.flag=TRUE)
ggplotly(g)

save_plot(filename=paste0("plots/", "Jun_Elk1_Fosl2_Mapk", "_log_gene_profile_genotype.pdf"), plot=g) 

Group gene profiles by condition

g <- geneGroupProfileRowsRev(normalized.counts=normExprData, design.matrix=designMatrix,
            gene.names=c("Nr1d1", "Fabp7", "Per3"),
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE, log.flag=TRUE)
ggplotly(g)

save_plot(filename=paste0("plots/", "Nr1d1_Fabp7_Per3", "_log_gene_profile_condition.pdf"), plot=g)

g <- geneGroupProfileRowsRev(normalized.counts=normExprData, design.matrix=designMatrix,
            gene.names=c("Jun", "Elk1", "Fosl2", "Mapk1", "Mapk3", "Mapk11"),
            res.o=de.genes.symb, show.plot=TRUE, plotly.flag=FALSE, log.flag=TRUE)
ggplotly(g)

save_plot(filename=paste0("plots/", "Jun_Elk1_Fosl2_Mapk", "_log_gene_profile_condition.pdf"), plot=g)